research-article

Cross-Domain Recommendation based on Heterogeneous information network with Adversarial learning

Authors:

Xianzhong XieAuthors Info & Claims

ISMSI '21: Proceedings of the 2021 5th International Conference on Intelligent Systems, Metaheuristics & Swarm Intelligence

Pages 65 - 70

https://doi.org/10.1145/3461598.3461609

Published: 10 August 2021 Publication History

Abstract

In this paper, based on the heterogeneous information network, we propose a cross-domain recommendation model by integrating adversarial learning (Cross-Domain Recommendation based on Heterogeneous information network with Adversarial learning, CDR-HA). Using information from other domains to alleviate the target data sparseness of the domain can improve the accuracy and performance of recommendations. In this paper, we focus on the cross-domain recommendation. Firstly, due to the differences in the feature distributions of the same users in different domains, we use the HIN2Vec algorithm to extract the user's feature distribution in the network based on the heterogeneous information network. Secondly, we propose a multi-domain feature filtering method, which maximizes the difference in the distribution of different domains based on Wasserstein Distance to preserve the differences in the feature distributions of users in different domains. Then, separately establish a classifier for each domain, we consider the results of the two classifiers comprehensively, and take the best as the final result. We apply the proposed model to two datasets and experimental results demonstrate that our approach outperforms state-of-the-art recommender baselines.

References

[1]

Liu Y, Zhao L, Dynamic Bayesian Logistic Matrix Factorization for Recommendation with Implicit Feedback. In IJCAI-18. 2018.

[2]

Yi Zhang and Xiao Hu. Heterogeneous knowledge transfer via domain regularization for improving cross-domain collaborative filtering. 2017 IEEE International Conference on Big Data (Big Data), Boston, MA, pages 3968-3974, 2017.

[3]

Meng Liu, Jianjun Li, Guohui Li, and Peng Pan. Cross Domain Recommendation via Bi-directional Transfer Graph Collaborative Filtering Networks. In CIKM '20), pages 885-894, 2020.

Digital Library

[4]

Taoyang Fu Tao, Wangchien Lee and Lei Zhen. HIN2Vec: Explore Metapaths in Heterogeneous Information Networks for Representation Learning. In Pro ACM CIKM Conf., pages 1797-1806, 2017.

[5]

Qian Zhang, Jie Lu, Dianshuang Wu, Cross-Domain Recommender System With Kernel-Induced Knowledge Transfer for Overlapping Entities. In IEEE Transactions on Neural Networks and Learning Systems, vol. 30, no. 7, pages 1998-2012, 2019.

[6]

Muhammad Murad Khan, Roliana Ibrahim, and Imran Ghani. Cross Domain Recommender Systems: A Systematic Literature Review. ACM Comput. Surv. 50, 3, Article 36, 34 pages, 2017.

[7]

M. Pratama, J. Lu, and G. Zhang. Evolving type-2 fuzzy classifier. IEEE Trans. Fuzzy Syst., vol. 24, no. 3, pages 574-589, 2016.

Digital Library

[8]

Ajit P Singh and Geoffrey J Gordon. Relational learning via collective matrix factorization. In Proc. 14th ACM SIGKDD Int. Conf. Knowl. Discovery Data Mining, pages 650–658, 2008.

[9]

Meng Jiang, Peng Cui; Xumin Chen, Fei Wang, Wenwu Zhu, Shiqiang Yang. Social recommendation with cross-domain transferable knowledge. IEEE Trans. Knowl. Data Eng., vol. 27, no. 11, pages 3084-3097, 2015.

Digital Library

[10]

Chuan Shi, Philip S. Yu. Heterogeneous Information Network Analysis and Applications. Data Analytics, pages 3–10, 2017.

[11]

Jiangyi Yin, Yuchun Guo, Yishuai Chen. Heterogenous Information Network Embedding Based Cross-Domain Recommendation System. 2019 International Conference on Data Mining Workshops (ICDMW). 2019.

[12]

Jian Shen, Yanru Qu, Weinan Zhang, Yong Yu. Wasserstein Distance Guided Representation Learning for Domain Adaptation. 2017.

[13]

Weiping Song, Zhiping Xiao, Yifan Wang, Session-Based Social Recommendation via Dynamic Graph Attention Networks. In WSDM '19, pages 555-563, 2019.

[14]

Hanxin Wang, Daichi Amagata, Takuya Makeawa. A DNN-based Cross-Domain Recommender System for Alleviating Cold-Start Problem in E-commerce. IEEE Open Journal of the Industrial Electronics Society, pages 1-1, 2020.

[15]

Wenjing Fu, Zhaohui Peng, Senzhang Wang, Yang Xu, and Jin Li. Deeply Fusing Reviews and Contents for Cold Start Users in Cross-Domain Recommendation Systems. In the Thirty-Third AAAI, pages 94-101, 2019.

Digital Library

[16]

Tong Man, Huawei Shen, Xiaolong Jin, Cross-Domain Recommendation: An Embedding and Mapping Approach. Twenty-Sixth International Joint Conference on Artificial Intelligence, pages 2464-2470, 2017.

[17]

Xiangnan He, Lizi Liao, Hanwang Zhang, Chua. Neural Collaborative Filtering. In ACM Conf. WWW, pages 173-182, 2017.

[18]

Zhang Y, Liu Y, Han P, Learning Personalized Itemset Mapping for Cross-Domain Recommendation. In IJCAI-PRICAI-20. 2020.

[19]

Liu Y, Xiao Y, Wu Q, Diversified Interactive Recommendation with Implicit Feedback[J]. Proceedings of the AAAI Conference on Artificial Intelligence, 34(4):4932-4939, 2020.

[20]

Han P, Li Z, Liu Y, Contextualized Point-of-Interest Recommendation. In IJCAI-PRICAI-20. 2020.

[21]

Yong Liu, Peilin Zhao, Aixin Sun, and Chunyan Miao. A boosting algorithm for item recommendation with implicit feedback. In Proceedings of the 24th International Conference on Artificial Intelligence (IJCAI'15). AAAI Press, pages 1792–1798, 2015.

Digital Library

[22]

Wu Q, Liu Y, Miao C, PD-GAN: Adversarial Learning for Personalized Diversity-Promoting Recommendation. In IJCAI-19. 2019.

Cited By

Li PBrost BTuzhilin A(2022)Adversarial Learning for Cross Domain RecommendationsACM Transactions on Intelligent Systems and Technology10.1145/354877614:1(1-25)Online publication date: 9-Nov-2022
https://dl.acm.org/doi/10.1145/3548776
Jiang HXing XLi SChen JJia Z(2022)Graph Neural Networks based Recommendation Methods in Different Scenarios: A Survey2022 8th Annual International Conference on Network and Information Systems for Computers (ICNISC)10.1109/ICNISC57059.2022.00135(660-666)Online publication date: Sep-2022
https://doi.org/10.1109/ICNISC57059.2022.00135

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ISMSI '21: Proceedings of the 2021 5th International Conference on Intelligent Systems, Metaheuristics & Swarm Intelligence

April 2021

87 pages

ISBN:9781450389679

DOI:10.1145/3461598

Copyright © 2021 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Publication History

Published: 10 August 2021

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The Key Science and Technology Research Program of Chongqing Municipal Education Commission

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ISMSI 2021

ISMSI 2021: 2021 5th International Conference on Intelligent Systems, Metaheuristics & Swarm Intelligence

April 10 - 11, 2021

Victoria, Seychelles

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Cited By

Li PBrost BTuzhilin A(2022)Adversarial Learning for Cross Domain RecommendationsACM Transactions on Intelligent Systems and Technology10.1145/354877614:1(1-25)Online publication date: 9-Nov-2022
https://dl.acm.org/doi/10.1145/3548776
Jiang HXing XLi SChen JJia Z(2022)Graph Neural Networks based Recommendation Methods in Different Scenarios: A Survey2022 8th Annual International Conference on Network and Information Systems for Computers (ICNISC)10.1109/ICNISC57059.2022.00135(660-666)Online publication date: Sep-2022
https://doi.org/10.1109/ICNISC57059.2022.00135

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